Underwater Federated Learning: Empowering Autonomous Underwater Vehicle Swarm with Online Learning Capabilities

Autonomous underwater vehicles (AUVs) are increasingly utilized across various domains, employing diverse machine learning (ML) algorithms to enhance functionality. However, the dynamic underwater environment, characterized by high temporal and spatial variability, makes offline-trained models on static datasets inadequate for practical AUV operations. This necessitates the integration of online learning capabilities that can adapt to changing conditions in real time. The quality of training data plays a crucial role in the performance of these models, and the ability to utilize distributed data from multiple AUVs can be beneficial. Nonetheless, the challenge lies in the limited communication resources available underwater. The typical acoustic communication rates, which are just in the tens of kilobits per second, pose a significant barrier to implementing centralized ML strategies that require extensive data sharing among AUVs. To overcome these hurdles, we propose an underwater federated learning (UFL) framework that incorporates model pruning and gradient quantization. This approach aims to establish a communication-efficient distributed learning paradigm. Furthermore, we have derived a closed-form expression to quantify the upper bound of the convergence error, which highlights the impact of pruning and quantization on the federated learning (FL) convergence. Additionally, we utilize a heuristic algorithm to optimize the pruning and quantization strategies, aiming to minimize the convergence error while adhering to delay constraints. The effectiveness of our proposed framework is demonstrated through its application in a cooperative navigation task involving multiple AUVs, showing significant resource conservation and enhanced operational efficiency.